Test: Trees - 1 - Software Development MCQ

Trees are commonly used to represent hierarchical structures, such as family trees, directory structures, etc. They consist of nodes connected by edges, where each node can have zero or more child nodes.

In a binary search tree (BST), the left subtree of a node contains only values smaller than the node's value, and the right subtree contains only values greater than the node's value. This property enables efficient searching, insertion, and deletion operations in a BST.

Disjoint Set Union (DSU), also known as Union-Find, is a data structure that efficiently performs union and find operations on disjoint sets. It is commonly used to solve problems involving connectivity or grouping of elements.

In a balanced binary search tree, the height of the tree is logarithmic with respect to the number of elements. Hence, the time complexity of searching for an element in a balanced BST is O(log n).

Fenwick trees, also known as Binary Indexed Trees (BIT), are efficient data structures for range sum queries and point updates. They support both update and query operations in O(log n) time complexity.

The code snippet creates a binary tree with three nodes. The line cout << root->left->data << endl; prints the value of the data member of the left child of the root node, which is 20.

A tree is most commonly represented using a linked list data structure where each node contains a value and references to its child nodes.

Inorder traversal visits the left subtree, then the root, and finally the right subtree of a binary tree.

Trees are not directly used for sorting algorithms. Sorting algorithms typically use arrays or linked lists.

In a binary search tree (BST), the time complexity to search for an element is O(log n) in the worst case.